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Iterative merging heuristics for correlation clustering

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Author

Summary, in English

A straightforward natural iterative heuristic for correlation clustering in the general setting is to start from singleton clusters and whenever merging two clusters improves the current quality score merge them into a single cluster. We analyse the approximation and complexity aspects of this heuristic and its three simple deterministic or random refinements.

Department/s

Publishing year

2014

Language

English

Pages

105-117

Publication/Series

International Journal of Metaheuristics

Volume

3

Issue

2

Document type

Journal article

Publisher

Inderscience Publishers

Topic

  • Computer Science

Keywords

  • Randomised algorithms
  • Time complexity
  • Approximation algorithms
  • Correlation clustering
  • Graph clustering

Status

Published

ISBN/ISSN/Other

  • ISSN: 1755-2184